A computed tomography (CT) imaging system typically includes an x-ray source that projects a fan-shaped x-ray beam through an object being imaged, such as a patient, to an array of radiation detectors. The beam is collimated to lie within an X-Y plane, generally referred to as an “imaging plane”. Intensity of radiation from the beam received at the detector array is dependent upon attenuation of the x-ray beam by the object. Attenuation measurements from each detector are acquired separately to produce a transmission profile.
The x-ray source and the detector array are rotated within a gantry and around the object to be imaged so that a projection angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements (such as integral projection data from the detector array at one gantry angle) is referred to as a “view”. A “scan” of the object comprises a set of views made at varying projection angles, during one revolution of the x-ray source and detector array.
In an axial scan, the projection data is processed to construct an image that corresponds to a two-dimensional slice taken through the object. For discrete slices, iterative reconstruction of a full field of view may be performed in order to increase image quality. Iterative reconstruction refers to a method which forms an image by repeatedly adjusting an existing estimate according to the quality of a match between measured data and simulated measurements from a current estimate of the image. The quality of the match may also be affected by consideration of the characteristics of the image alone, such as its smoothness and/or satisfaction of a pre-established model. Multiple iterations are performed to create a resulting reconstructed image that approximately matches the acquired projection data. A full set of reconstructed images is referred to as a 3-D reconstruction, since the set is formed into a three dimensional representation of the object with each image pixel or picture element corresponding to a single voxel or volume element in the 3-D reconstruction.
To reduce the total scan time required for multiple slices, a “helical” scan may be performed. Helical scan techniques allow for large volumes to be scanned at a quicker rate using a single photon source. To perform a “helical” scan, the patient is moved along the z-axis, the axis about which the gantry rotates, synchronously with the rotation of the gantry, while data for a prescribed number of slices are acquired. Such a system generates a single helix from a fan beam helical scan. The helix mapped out by the fan beam yields projection data from which images in each prescribed slice may be reconstructed. In addition to reducing scan time, helical scanning provides other advantages such as better use of injected contrast, improved image reconstruction at arbitrary locations, and better three-dimensional images.
Traditionally, direct analytical algorithms, such as the Filtered Back-Projection (FBP) algorithm, have been used to reconstruct images from CT data. Iterative techniques, such as the Maximum A Posteriori Iterative Coordinate Descent (MAP-ICD) algorithm, have also been recently considered for reconstruction of volumetric CT data to provide means to improve general image quality over conventional techniques. It has been demonstrated that reduced noise, enhanced resolution, better low contrast performance, and reduced artifacts, can all be achieved with iterative reconstruction of clinical images. Iterative algorithms generally work by optimizing over a cost function formed of a data fit term and a penalization term. The data fit term describes a model wherein synthesized projections from an image estimate must match the acquired projection measurements, and may include a statistical weighting to apply different degrees of confidence to each datum depending on its noise characteristics. The penalization term typically enforces a smoothness constraint on the reconstructed images, and may treat differently homogeneous regions and regions with a large local gradient such as edges and organ boundaries. An iterative algorithm is applied to iteratively refine an image estimate from a set of initial conditions so as to minimize the resulting global cost function. When the minimum of the cost function has been achieved, the iterative algorithm has converged to the solution. For multi-slice CT data, the solution is a three-dimensional volume of image estimates that best matches the acquired data based on the model described in the cost function.
One of the algorithms developed for iterative optimization of the cost function is the Iterative Coordinate Descent (ICD) algorithm. The ICD algorithm attacks the problem of optimization of the multi-dimensional cost function as a sequence of one-dimensional greedy updates for fast convergence. Each pixel in image space may be updated independently of all the other pixels, and forms a single dimension in the N-dimensional problem, where N is the total number of pixels in the imaged volume. In order to speed up convergence, a spatially non-homogeneous version of ICD has been introduced, which is referred to as NH-ICD. With this NH-ICD approach, the order in which the pixels are selected for update is optimized to focus computation on the regions of the reconstructed volume that are most in need of updating. In conventional ICD, all pixels are updated once and once only per iteration. With NH-ICD, some voxels may be updated more often than others based on the update needs for appropriate convergence. This leads to the definition of an “equivalent iteration” to describe the amount of computation equivalent to a single update of the entire image volume, where each “equivalent iteration” with NH-ICD may correspond to only to a partial image update. NH-ICD functions to speed up convergence for a reconstruction over the full field of view.
In the clinical environment, however, all images are reconstructed to zoom over the portion of the anatomy relevant for diagnosis, rather than reconstructing cross-sections of the whole patient. In order to reconstruct a targeted area, iterative reconstruction algorithms differ from conventional techniques such as FBP in that they generally require reconstructing the entire field of view, which includes all the objects measured by the CT system. Such a full field reconstruction is performed with iterative reconstruction algorithms in order to achieve good image quality. This is due to the fact that iterative reconstruction requires the consideration of all possible sources of x-ray attenuation along the whole path lengths between the x-ray source and the detector. However, this implies significant computational cost in targeted reconstruction of a small area. For instance, reconstructing a 512×512 image in 35 cm field of view where the bore of the CT scanner is 70 cm in diameter requires iterating over a 1024×1024 image in 70 cm field of view to guarantee that all possible sources of x-ray attenuation are captured in the reconstruction. That would be four times the number of voxels reconstructed with FBP.
So far, multi-resolution techniques have been developed for targeted iterative reconstruction. A multi-resolution technique starts with a low-resolution reconstruction (for instance a 512×512 700 mm reconstruction), which is then followed by a high-resolution targeted reconstruction (for instance, a 1024×1024 700 mm reconstruction where only the center 35 cm are updated). In the low-resolution reconstruction, all pixels in the 700 mm field of view are reconstructed. The low-resolution reconstructed images are then interpolated up and used as the initial conditions for the high-resolution reconstruction. The high-resolution reconstruction starts with a full forward projection of the initial images in full field of view and then reconstructs only in a region of interest (ROI). This method works well but is inefficient because: 1) all the pixels are reconstructed in low-resolution regardless of the final target ROI, and 2) changing resolution requires a full forward projection in high-resolution to eliminate any possible errors between the low-resolution and high-resolution synthesized projections. A more effective method is needed to improve the speed and quality of the targeted reconstructions.